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pass doctests

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This commit is contained in:
Benjamin Bertrand 2017-01-27 17:52:58 +03:00
parent 603e6cab84
commit a761770510
2 changed files with 51 additions and 50 deletions
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90
notes_tools/tools/df_marks_manip.py
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@ -29,7 +29,7 @@ def note_to_rep(x):
... "Trimestre": ["1"]*12,
... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.67, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> note_to_rep(df.loc[0])
@ -136,18 +136,18 @@ def compute_marks(df):
... }
>>> df = pd.DataFrame(d)
>>> compute_marks(df)
0 1.000000
1 0.330000
2 2.000000
3 1.500000
4 0.666667
5 2.000000
6 0.666000
7 1.000000
8 1.500000
9 1.000000
10 1.333333
11 2.000000
0 1.00
1 0.33
2 2.00
3 1.50
4 0.67
5 2.00
6 0.67
7 1.00
8 1.50
9 1.00
10 1.33
11 2.00
dtype: float64
"""
return df[["Note", "Niveau", "Bareme"]].apply(note_to_mark, axis=1)
@ -175,7 +175,7 @@ def compute_latex_rep(df):
3 1.5
4 \RepU
5 \RepT
6 0.666
6 0.67
7 1
8 1.5
9 1
@ -203,18 +203,18 @@ def compute_normalized(df):
>>> df = pd.DataFrame(d)
>>> df["Mark"] = compute_marks(df)
>>> compute_normalized(df)
0 1.000000
1 0.330000
2 1.000000
3 0.750000
4 0.333333
5 1.000000
6 0.666000
7 1.000000
8 0.750000
9 0.500000
10 0.666667
11 1.000000
0 1.00
1 0.33
2 1.00
3 0.75
4 0.33
5 1.00
6 0.67
7 1.00
8 0.75
9 0.50
10 0.67
11 1.00
dtype: float64
"""
return df["Mark"] / df["Bareme"]
@ -239,7 +239,7 @@ def compute_exo_marks(df):
... "Trimestre": ["1"]*12,
... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.67, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> df["Mark"] = compute_marks(df)
@ -281,7 +281,7 @@ def compute_eval_marks(df):
... "Trimestre": ["1"]*12,
... "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... "Note":[1, 0.33, 2, 1.5, 1, 3, 0.67, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> df["Mark"] = compute_marks(df)
@ -342,33 +342,33 @@ def digest_flat_df(flat_df):
>>> quest_df, exo_df, eval_df = digest_flat_df(df)
>>> quest_df[['Eleve', "Nom", "Mark", "Latex_rep", "Normalized", "Uniq_quest"]]
Eleve Nom Mark Latex_rep Normalized Uniq_quest
0 E1 N1 1.000000 1 1.000000 Ex1 Q1
1 E1 N1 0.330000 0.33 0.330000 Ex1 Q2
2 E1 N1 2.000000 2 1.000000 Ex2 Q1
3 E1 N1 1.500000 1.5 0.750000 Ex2 Q2
4 E1 N2 0.666667 \RepU 0.333333 Ex1 Q1
5 E1 N2 2.000000 \RepT 1.000000 Ex2 Q1
0 E1 N1 1.00 1 1.00 Ex1 Q1
1 E1 N1 0.33 0.33 0.33 Ex1 Q2
2 E1 N1 2.00 2 1.00 Ex2 Q1
3 E1 N1 1.50 1.5 0.75 Ex2 Q2
4 E1 N2 0.67 \RepU 0.33 Ex1 Q1
5 E1 N2 2.00 \RepT 1.00 Ex2 Q1
6 E2 N1 NaN ?? NaN Ex1 Q1
7 E2 N1 0.000000 0 0.000000 Ex1 Q2
8 E2 N1 0.000000 0 0.000000 Ex2 Q1
7 E2 N1 0.00 0 0.00 Ex1 Q2
8 E2 N1 0.00 0 0.00 Ex2 Q1
9 E2 N1 NaN ?? NaN Ex2 Q2
10 E2 N2 NaN \NoRep NaN Ex1 Q1
11 E2 N2 NaN \NoRep NaN Ex2 Q1
>>> exo_df[['Eleve', "Nom", "Exercice", "Mark", "Normalized"]]
Eleve Nom Exercice Mark Normalized
0 E1 N1 Ex1 1.5 0.750
1 E1 N1 Ex2 3.5 0.875
2 E1 N2 Ex1 1.0 0.500
3 E1 N2 Ex2 2.0 1.000
4 E2 N1 Ex1 0.0 0.000
5 E2 N1 Ex2 0.0 0.000
0 E1 N1 Ex1 1.5 0.75
1 E1 N1 Ex2 3.5 0.88
2 E1 N2 Ex1 1.0 0.50
3 E1 N2 Ex2 2.0 1.00
4 E2 N1 Ex1 0.0 0.00
5 E2 N1 Ex2 0.0 0.00
6 E2 N2 Ex1 NaN NaN
7 E2 N2 Ex2 NaN NaN
>>> eval_df
index Eleve Nom Trimestre Bareme Date Mark Normalized
0 0 E1 N1 1 6.0 16/09/2016 5.0 0.833333
1 1 E2 N1 1 6.0 16/09/2016 0.0 0.000000
2 0 E1 N2 1 4.0 01/10/2016 3.0 0.750000
0 0 E1 N1 1 6.0 16/09/2016 5.0 0.83
1 1 E2 N1 1 6.0 16/09/2016 0.0 0.00
2 0 E1 N2 1 4.0 01/10/2016 3.0 0.75
3 1 E2 N2 1 4.0 01/10/2016 NaN NaN
"""
df = flat_df.copy()

1
notes_tools/tools/eval_tools.py
View File

@ -50,6 +50,7 @@ def keep_only_presents(quest_df, exo_df, eval_df, presents):
def students_pov(quest_df, exo_df, eval_df):
"""
>>> from .df_marks_manip import digest_flat_df
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
... "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],